actual submission

README.md

@@ -1,15 +1,7 @@
 ---
 title: "Machine Learning Methods for Orbital Debris Characterization: Report 1"
-description: |
-  A short description of the post.
-author:
-  - name: Anson Biggs
-  url: https://ansonbiggs.com
+author: Anson Biggs
 date: 2022-02-14
-output:
-  distill::distill_article:
-    self_contained: false
-draft: true
 ---
 
 ## Gathering Data
@@ -36,14 +28,7 @@ Physical
 
 The entire file of the compiled parts properties from Fusion 360 can be seen [here.](https://gitlab.com/orbital-debris-research/directed-study/report-1/-/blob/main/compiled.csv) This method gave 22 columns of data, but most of the columns are unsuitable for the characterization of 3D geometry. The only properties considered must be scalars independent of a model's position orientation in space. Part of the data provided was a moment of inertia tensor. The tensor was processed down to $I_x$, $I_y$, and $I_z$, which was then used to calculate an $\bar{I}$. Then bounding box length, width, and height were used to compute the total volume that the object takes up. In the end, the only properties used in the analysis of the parts were: mass, volume, density, area, bounding box volume, $\bar{I}$, and material. Some parts also had to be removed because the final dataset is 44 rows and 7 columns. Below is a _Splom_ plot which is a great way to visualize data of high dimensions. As you can see, most of the properties correlate with one another.
 
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-  type="text/html"
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+![](prepped.svg)
 
 Now that the data is processed and clean, characterization in Matlab can begin. The original idea was to perform _PCA_, but the method had difficulties producing meaningful results. This is likely because the current dataset is tiny for machine learning and the variation in the data is high. The application of _PCA_ will be revisited once the dataset grows. The first step for characterization is importing our data into Matlab.
 
@@ -71,14 +56,7 @@ Then plotting Volume vs. Mass using our clusters produces the following plot. Th
 
 Below is another _Splom_, but with the clusters found above. Since the _k-means_ only used Mass and Volume to develop its clusters, some of the properties do not cluster well against each other. This is also a powerful cursory glance at what properties are correlated.
 
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-<embed
-  type="text/html"
-  src="prepped_clustered.html"
-  width="100%"
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+![](prepped_clustered.svg)
 
 ## Next Steps